International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 09 Issue: 09 | Sep 2022
p-ISSN: 2395-0072
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CRIME ANALYSIS AND PREDICTION USING MACHINE LEARNING Roopa1, Prof. Thouseef Ulla Khan2 1PG
Scholar (MCA), Dept of MCA, Vidya Vikas Institute of Engineering And Technology, Mysore ,Karnataka, India Professor, Dept of MCA, Vidya Vikas Institute of Engineering And Technology, Mysore ,Karnataka, India ---------------------------------------------------------------------***--------------------------------------------------------------------discovery of criminal "hot spots," which show regions with a Abstract – Crime is one of our society's most serious and 2Assistant
high concentration of crime, has been proven valuable by the police. Data mining approaches can yield significant findings from crime report databases. Crime analysis is the first phase in the study of crime. Criminal analysis is the exploration, interrelationship, and detection of relationships between numerous crimes and crime variables. This analysis aids in the creation of statistics, queries, and maps on demand. It also aids in determining whether a crime has occurred in a certain recognized location.
pervasive problems, and preventing it is a critical duty. This necessitates keeping note of all offences and creating a database for future reference. The present issue is keeping a reliable crime record and analysing this data to aid in the prediction and resolution of future crimes. The objective of this paper is to analyze dataset, which consist of numerous crimes and predicting the type of crime, which may happen in future depending upon various conditions. In this project, we will be using the technique of machine learning and data science for crime prediction of Indian crime data set. Crime analysis and prediction is a methodical way to spotting crime. This algorithm can anticipate and depict crime-prone areas. Using the notion of machine learning, we may extract previously unknown, meaningful information from unstructured data. The extraction of new information is anticipated using current datasets. Crime is a perilous and widespread societal issue that affects people all around the world. Crime has an impact on people's quality of life, economic prosperity, and the nation's reputation. To safeguard their communities from crime, modern technology and novel techniques to enhancing crime analytics are required. We present a system that can analyse, identify, and forecast various crime probabilities in a given location. This study describes many sorts of criminal analysis and crime prediction using machine learning approaches.
1.1 Objectives The prediction using data mining techniques that is prediction rules. Frequent patterns are extracted based on the criteria’s like crime type. Prediction is done based on the previous year datasets. The prediction report consists of all the datasets from the year 2012-2020.the year wise comparison is shown based on the state wise datasets. The clustering algorithm can be perform based on every datasets based on each year wise comparison is made.
1.2 Scope The primary goals of crime evaluations are as follows: 1. Identifying crime tendencies by study of existing crimes and criminal information 2. Using geographic distribution to forecast crime of available information as well as prediction ofcrime total utilizing various datamining techniques 3. Criminal detection
Key Words: Decision trees, linear regression, and k-means clustering
1. INTRODUCTION
2. Existing System:
The crime data rate is growing on a daily basis because current technology and high-tech ways assist criminals in carrying out their illicit actions. Burglary, arson, and other crimes, according to the Crime Record Bureau have escalated, as have crimes such as murder, rape, abuse, gang rap, and so on. Data on crime will be gathered from numerous blogs, news sites, and websites. The massive amount of data is utilized to create a record. A database of crime reports. The knowledge gained via data mining techniques will be useful in lowering crime by making it easier to discover the perpetrators and the regions most affected by crime.
This algorithm can forecast high-risk areas for crime and show crime-prone areas. Using the concept of data mining, we may draw previously undiscovered, pertinent information from unstructured data. Disadvantages:
They exhibited lower prediction accuracy using this technique.
The outcomes of this approach are not ideal.
When applied to a crime dataset, data mining techniques produce good results. The information generated from data mining techniques can assist the police department. The
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